Recurrent matching networks of spatial alignment learning for person re-identification

Lin, Lan; Zhang, Dan; Zheng, Xin; Ye, Mao; Guo, Jiuxia

doi:10.1007/s11042-019-08364-9

Cited by 2 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Person reidentification aims to match a probe pedestrian image in candidate pedestrian images across disjoint cameras [1][2][3]. In recent years, deep learning algorithms [18][19][20][21][22][23] for person reidentification indicate distinct superiority on matching accuracy. Most of these methods pay attention to extract distinguishable features from holistic pedestrian images [7][8][9][22][23][24][25].…”

Section: Person Reidentificationmentioning

confidence: 99%

Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Lin

Huang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.

show abstract